Comment by SubiculumCode
17 hours ago
The major concern with looped transformers is that makes it more difficult to monitor model alignment. When more processing occurs within latent space without outputting text, that means less effective, frequent chain-of-thought monitoring, and the potential for greater un-monitored latent-space shenanigan.
I'm not sure. That paper from anthropic talked about monitoring j space, presumably those same techniques would work here?
I am no expert, but I think it is this: 1) We have few effective tools at monitoring alignment right now, and chain of thought is one of the more effective. 2) Monitoring latent space may be possible, but I do not think it is even close to being a solved problem, nor whether it is possible at scale and outside of controlled problem areas. 3) Finally, more recursion within latent space may complexify the latent representations, not simplify them.
This is silly, the entire reason why chain of thought even exists is to let the LLM "think independently" instead of minimizing the deviation from the supervised training sample. It's an intentional scratch pad for intermediate data. The loose monitoring is kind of the entire point.